def selu(x): """Scaled Exponential Linear Unit. (Klambauer et al., 2017). Arguments: x: A tensor or variable to compute the activation function for. Returns: Tensor with the same shape and dtype as `x`. References: - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 return scale * K.elu(x, alpha)
def selu(x): """Scaled Exponential Linear Unit. (Klambauer et al., 2017). Arguments: x: A tensor or variable to compute the activation function for. Returns: Tensor with the same shape and dtype as `x`. # Note - To be used together with the initialization "lecun_normal". - To be used together with the dropout variant "AlphaDropout". """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 return scale * K.elu(x, alpha)
def selu(x): """Scaled Exponential Linear Unit. (Klambauer et al., 2017). Arguments: x: A tensor or variable to compute the activation function for. Returns: Tensor with the same shape and dtype as `x`. # Note - To be used together with the initialization "lecun_normal". - To be used together with the dropout variant "AlphaDropout". """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 return scale * K.elu(x, alpha)
def elu(x, alpha=1.0): return K.elu(x, alpha)
def call(self, inputs): return K.elu(inputs, self.alpha)
def elu(x, alpha=1.0): return K.elu(x, alpha)
def call(self, inputs): return K.elu(inputs, self.alpha)